Abstract / Summary The inability to engage in spoken communication is among the most debilitating of all human conditions. In the field of communication disorders, a speech-language pathologist?s (SLP?s) perceptual evaluation of the quality of speech production is the gold standard for assessment and for documenting treatment progress. However, decades of research has confirmed that auditory-perceptual judgments of speech are inherently biased, which compromises reliability. The reason is that the human perceptual system is adaptive, with perceptual bias accrued by working with an individual across multiple treatment sessions, or by working with patient populations across a career. Thus, to reliably document treatment outcomes subjectively, it is necessary to involve multiple, unfamiliar listeners. This is untenable in most clinical settings, which means that subjective impressions are made by the treating clinicians. The reliance on subjective evaluation directly undermines the quality of clinical practice and a clinician?s ability to demonstrate the efficacy of an intervention. Aural Analytics has developed new objective acoustic speech metrics that track with disease progression in neurological disorders. Our technology is based on a strong scientific premise and has been adopted early by pharmaceutical companies and neurologists in clinical research. We have collected and analyzed tens of thousands of speech samples using our technology, and the results are demonstrating that our measures are robust, reliable, and more sensitive to longitudinal changes in speech than are other existing outcome measures. With this proposal we aim to translate this technology to SLP clinical practice by developing an app- based automated solution that provides objective speech-based outcomes and to solve the clinical crises created by reliance on subjective ratings. Our SBIR Phase I project will have two specific aims. In SA1 we will develop a stand-alone application that SLPs can use to objectively evaluate the effects of behavioral intervention. This will require translating the existing algorithms to Python, developing an Application Programming Interface, and designing a mobile application that speech-language pathologists can use in clinical practice. In SA2, in collaboration with Barrow Neurological Institute, we will validate the usability of the application in a clinical setting through beta testing with SLPs. The deliverable of this proposal will be a fully- functional mobile application with real time metrics, that will be ready for a clinical evaluation in a follow-on Phase II proposal.